UNCORRECTED PROOF

Thompson et al. Translational Psychiatry

https://doi.org/10.1038/s41398-020-0705-1

ranslational Psychiatry

REVIEW ARTICLE Open Access

ENIGMA and global neuroscience: a decade of

large-scale studies of the brain in health and

disease across more than 40 countries

Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta

Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health

and disease. Building on large-scale genetic studies that discovered the ﬁrst robustly replicated genetic loci associated

with brain metrics, ENIGMA has diversiﬁed into over 50 working groups (WGs), pooling worldwide data and expertise

to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on

speciﬁc psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences,

or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized

analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These

international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major

depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-

deﬁcit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent

ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating

disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here,

we summarize the ﬁrst decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges

encountered along the way. We highlight the advantages of collaborative large -scale coordinated data analyses for

testing repr oducibility and robustness of ﬁndings, offering the opportunity to identify brain sy stems involved in clinical

syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial

factors.

Introduction

The ENIGMA (Enhancing NeuroImaging Genetics

through

Meta Analysis) Consortium is a collaboration of

more than 1400 scientists from 43 countries studying the

human brain. ENIGMA started 10 years ago, in 2009, with

the initial aim of performing a large-scale neuroimaging

genetic study, and has since diversiﬁed into 50 working

groups (WGs), pooling worldwide data, resources and

expertise to answer fundamental questions in neu-

roscience, psychiatry, neurology, and genetics (Fig. 1

shows a world map of participating sites, broken down by

working group). Thirty of the ENIGMA WGs focus on

speciﬁc psychiatric and neurologic conditions. Four study

different aspects of development and aging. Others study

key transdiagnostic constructs, such as irritability, and the

importance of

evolutionarily interesting genomic regions

in shaping human brain structure and function. Central to

the success of these

WGs are the efforts of dedicated

methods development groups within ENIGMA. There are

currently 12 WGs that develop and disseminate multi-

scale and ‘big data’ analysis pipelines to facilitate harmo-

nized analyses using genetic and epigenetic data,

multimodal (anatomical, diffusion, functional) magnetic

Correspondence: Paul M. Thompson (pthomp@usc.edu)

Full list of author information is available at the end of the article.

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1234567890():,;

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resonance imaging (MRI) and spectroscopy (MRS) mea-

sures, in combination with genetic and epigenetic data,

and data from electroencephalography (EEG).

The Consortium has been a formidable force for dis-

covery and innovation in human brain imaging, sup-

porting more than 200 active studies. The disorder-

speciﬁc WGs have published the largest neuroimaging

studies to date in schizophrenia (SCZ; total N = 9572;

4474 cases)

, bipolar disorder (BD; total N = 6503; 2447

cases)

, major depressive disorder (MDD; total N =

10,105; 2148 cases)

, post-traumatic stress disorder

(PTSD; total N = 1868; 794 cases)

, substance use dis-

orders (SUD; total N = 3240; 2140 cases)

, obsessive-

compulsive disorder (OCD; total N = 3665; 1905 cases)

attention-deﬁcit/hyperactivity disorder (ADHD; total N =

4180; 2246 cases)

, autism spectrum disorder (ASD; total

N = 3222; 1571 cases)

, epilepsy ( N = total 3876; 2149

cases)

, and 22q11.2 deletion syndrome (22q11DS; total

N = 944; 474 cases)

. Key results of these studies are

summarized in Table 1. Building on this work, the focus

of the ENIGMA disorder-speciﬁc WGs now goes beyond

traditional diagnostic boundaries. As these ﬁrst large-scale

studies are being completed, ENIGMA is beginning to

identify shared and distinct neuroimaging patterns in

brain disorders with known genetic or clinical over-

lap

11,12

, and to delineate the role of transdiagnostic risk

factors (e.g., childhood trauma) and clinical phenomena

(e.g., suicidal thoughts and behaviors). In addition,

ENIGMA’s genetic studies are now analyzing imaging and

genetics data from more than 50,000 people to uncover

genetic markers that most robustly associated with brain

structure and function, or imaging derived neurobiologi-

cal traits related to various disease conditions

13–16

As we detail in this review, the ENIGMA Consortium

has made multiple, seminal contributions to neuroscience

and psychiatry, including (a) characterization of robust

neuroimaging proﬁles for various brain disorders, (b)

standardization of metrics used to assess clinical symp-

toms of patients across multiple research sites, and (c) use

of dimensional approaches that go beyond the

case–control comparisons of individuals with categorical

diagnoses, and further enable the investigation of speciﬁc

genetic, and environmental features or neurobiological

markers associated with disorder risk and treatment

Fig. 1 World Map of ENIGMA’s Working Groups. The ENIGMA Consortium has grown to include over 1400 participating scientists from over 200

institutions, across 43 countries worldwide. ENIGMA is organized as a set of 50 WGs, studying 26 major brain diseases (see color key). Each group

works closely with the others and consists of worldwide teams of experts in each brain disorder as well as experts in the major methods used to

study each disorder. The diseases studied include major depressive disorder, bipolar disorder, schizophrenia, substance use disorder, post-traumatic

stress disorder, attention-deﬁcit/hyperactivity disorder, obsessive-compulsive disorder, and autism spectrum disorder, and several neurological

disorders, including Parkinson’s disease, epilepsy, ataxia, and stroke. In recent years, new WGs were created that grew into worldwide consortia on

epilepsy (Whelan

), eating disorders (King

104

), anxiety disorders (Groenewold

107

), antisocial behavior, and infant neuroimaging.

Thompson et al. Translational Psychiatry Page 2 of 26

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Table 1 A Selection of key ﬁndings from ENIGMA’s Working Groups, along with key papers and current sample sizes.

Working group Number of datasets Total N (patient N) Age range

(in years)

Relevant publications Main ﬁndings

Clinical

22Q11DS 14 863 (533) 6–56 Villalon-Reina, Mol

Psychiatr, 2019; Sun

Widespread reductions in diffusivity, pronounced in

regions with major cortico-cortical and cortico-thalamic

ﬁbers; thicker cortical gray matter overall, but focal

thickness reduction in temporal and cingulate cortex;

cortical surface area showed pervasive reductions; lower

cortical surface area in individuals with larger

microdeletion; 22q-related psychosis associated with

lower cortical thickness and signiﬁcantly overlapped with

ﬁndings from ENIGMA-SCZ group.

Addiction/SUDs 118 18,823 (6,592) 7–68 Mackey

; Conrod

;

Mackey

Common neural substrate shared in dependence;

differential patterns of regional volume as biomarkers of

dependence on alcohol and nicotine; lower volume or

thickness observed, with greatest effects associated with

alcohol use disorder; insula and medial orbitofrontal

cortex affected, regardless of dependence.

ADHD 37 4180 (2246) 4–63 Hoogman

; Klein

; Zhang-

James

; Hess

;

Hoogman

Reduction in bilateral amygdala, striatal, and hippocampal

volumes in the ADHD population, especially in children;

lower cortical surface area values found in children with

ADHD, but not in adolescents or adults; lower surface area

associated with ADHD symptoms in the general

population in childhood; genetic association studies

suggest that genes involved in neurite outgrowth play a

role in ﬁndings of reduced volume in ADHD; gene-

expression studies imply that structural brain alterations in

ADHD can also be explained in part by the differential

vulnerability of these regions to mechanisms mediating

apoptosis, oxidative stress, and autophagy.

ASD 54 3583 (1774) 2–64 Postema

; van Rooij

Altered morphometry in the cognitive and affective parts

of the striatum, frontal cortex and temporal cortex in ASD.

BD 44 11,100 (3100) 8–86 Favre

; Nunes

; Hibar,

Mol Psychiatr, 2017; Hibar

Volumetric reductions in hippocampus and thalamus and

enlarged lateral ventricles in patients; thinner cortical gray

matter in bilateral frontal, temporal and parietal regions;

strongest effects on left pars opercularis, fusiform gyrus

and rostral middle frontal cortex in BD.

Eating Disorders 28 anorexia nervosa (AN); 12

bulimia nervosa (BN)

2531 (897 AN; 307 BN) 10–50 AN;

12–46 BN

Walton

Signs of inverse concordance between greater thalamus

volume and risk for anorexia nervosa (AN); variation in

gene DRD2 signiﬁcantly associated with AN only after

conditioning on its association with caudate volume;

genetic variant linked to LRRC4C reached signiﬁcance

after conditioning on hippocampal volume.

Epilepsy 24 3876 (2149) 18–55 Whelan

Patients with IGE showed volume reductions in the right

thalamus and lower thickness in the bilateral precentral

gyri; both MTLE subgroups showed volume reductions in

the ipsilateral hippocampus, and lower thickness in

extrahippocampal cortical regions, including the

precentral and paracentral gyri; lower subcortical volume

and cortical thickness were associated with a longer

duration of epilepsy in the all-epilepsies and right MTLE

groups.

HIV 12 1044 (all patients) 22–81 Nir

124

; Nir, MICCAI

201

;

Fouche, OHBM, 2015; Nir,

CNS, 2015

In the full group, subcortical volume associations

implicated the limbic system: lower current CD4+ counts

were associated with smaller hippocampal and thalamic

volumes; a detectable viral load was associated with

smaller hippocampal and amygdala volumes; limbic

effects were largely driven by participants on cART; in

subset of participants not on cART, smaller putamen

volumes were associated with lower CD4+ count.

MDD 38 14,249 (4379) 10–89 van Velzen

; Tozzi

;

Han

; Frodl

; Renteria

2017; Schmaal

; Schmaal

;

137

Signiﬁcantly lower hippocampal volumes; thinner

orbitofrontal cortex, anterior and posterior cingulate,

insula and temporal lobes cortex in adult MDD patients;

lower total surface area and regional reductions in frontal

regions and primary and higher-order visual,

somatosensory and motor areas in adoloescent MDD

patients; greater exposure to childhood adversity

associated with smaller caudate volumes in females,

independent of MDD; patients reporting suicidal plans or

attempts showed a smaller ICV volume compared to

controls.

OCD 38 3665 (1905) 5–65 Boedhoe, Front

Neuroinform, 2019; Hibar

;

Boedhoe

; Boedhoe

Subcortical abnormalities in pediatric and adult patients;

pallidum (bigger) and hippocampus (smaller) key in

adults, and thalamus (bigger) key in (unmedicated)

pediatric group; parietal cortex consistently implicated

both in children and adults; more widespread cortical

thickness abnormalities in medicated adults, and more

pronounced surface area deﬁcits (mainly in frontal

regions) in medicated pediatric OCD patients.

PTSD 16 3118 (1288) 17–85 Dennis

; Salminen

;

Logue

;O’Leary

;

Saemann, OHBM, 2018

Signiﬁcantly smaller hippocampi, on average, in

individuals with current PTSD compared with trauma-

exposed control subjects, and smaller amygdalae.

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outcome. The large scale and inclusivity of these ana-

lyses—in terms of populatio ns, sample sizes, numbers of

coordinating centers, a nd diversity of i maging and

genetic data—has been instrumental f or demo nstrating

robust associations between clinical factors and brain

alterations, and for stratifying patients with the same

diagnosis according to differential treatment out-

comes

10,17

. Thus, a valuable aspect of the existing

ENIGMA studies is the ability to identify the most

robust patt ern of non-invas ively measured neurobiolo-

gical features involved in clinical syndromes across

multiple samples that are more representative of the

global population. This also results in robust effect size

estimates, without the confounds of literature-based

meta-analyses based on published data with possible

publication bias (as noted in Kong et a l., in prep )

These data also provide a unique opportunity to assess

important sources of disease heterogeneity, including

key genetic, environmental, demographic, and psycho-

social factors. Here, we provide a synopsis of the ﬁrst

decade of ENIGMA’s activities and highlight the suc-

cesses and challenges encountered along the way.

Table 1 continued

Working group Number of datasets Total N (patient N) Age range

(in years)

Relevant publications Main ﬁndings

Schizophrenia 39 9572 (4474) 18–77 Guadalupe, 2019 (in press);

Holleran

; van Erp

;

Kelly

; Walton

;

Kochunov

; van Erp, Mol

Psychiatr, 2015

Positive symptom severity was negatively related to

bilateral STG thickness; widespread thinner cortex and

smaller surface area, largest effect sizes in frontal and

temporal lobe regions; smaller hippocampus, amygdala,

thalamus, accumbens and intracranial volumes; larger

pallidum and lateral ventricle volumes; widespread

reductions in FA, esp. in anterior corona radiata and

corpus callosum; higher mean and radial diffusivity; left

MOFC thickness signiﬁcantly associated with negative

symptom severity; link between prefrontal thinning and

negative symptom severity in schizophrenia.

CNV 37 16,889 (24 16p11.2 distal and 125

15q11.2 CNV carriers)

3–90 van der Meer

100

; Sonderby,

Mol Psychiatr

16p11.2 distal CNV: Negative dose-response associations

with copy number on intracranial volume and regional

caudate, pallidum and putamen volumes. 15q11.2 CNV:

Decrease in accumbens and cortical surface area in

deletion carriers and negative dose response on cortical

thickness.

Non-clinical

EEG 5 8425 5–73 Smit

Identiﬁed several novel genetic variants associated with

oscillatory brain activity; replicated and advanced

understanding of previously known genes associated with

psychopathology (i.e., schizophrenia and alcohol use

disorders); these psychopathological liability genes affect

brain functioning, linking the genes’ expression to speciﬁc

cortical/subcortical brain regions.

GWAS 34 22,456 3–91 Satizabal

; Grasby

; Hibar,

Nature Commun, 2017;

Adams, Nature Neurosci,

2016; Hibar

Over 200 genetic loci where common variation is

associated with cortical thickness or surface area; over 40

common genetic variants associated with subcortical

volumes.

Laterality 99 17,141 3–90 de Kovel

; Kong

(in

press); Postema

; Kong

153

;

Guadalupe, BIB, 2017

Average patterns of left-right anatomical asymmetry of

the healthy brain were mapped, as regards cortical

regional surface areas, thicknesses, and subcortical

volumes; fronto-occipital gradient in cortical thickness

asymmetry was found, with frontal regions generally

thicker on the left, and occipital regions on the right;

asymmetries of various structural measures were

signiﬁcantly heritable, indicating genetic effects that differ

between the two sides; age, sex and intracranial volume

affected some asymmetries, but handedness did not;

disorder case –control analyses revealed subtle reductions

of regional cortical thickness asymmetries in ASD, as well

as altered orbitofrontal surface area asymmetry; little

evidence for altered anatomical asymmetry was found in

MDD; pediatric patients with OCD showed evidence for

altered asymmetry of the thalamus and pallidum.

Lifespan 91 14,904 healthy individuals 2–92 Dima et al., 2015; Frangou

et al., 2019 (in prep)

Thickness in almost all cortical regions decreased

prominently in the ﬁrst two to three decades of life, with

an attenuated or plateaued slope afterwards; exceptions

to this pattern were entorhinal and temporopolar cortices

whose thickness showed an attenuated inverse U-shaped

relation with age, and anterior cingulate cortex, which

showed a U-shaped association with age; age at peak

cortical thickness was 6–7 years for most brain regions.

Plasticity 36 10,199 (2242) 6–97 Brouwer

; Brouwer

Heritability estimates of change rates were generally

higher in adults than in children suggesting an increasing

inﬂuence of genetic factors explaining individual

differences in brain structural changes with age; for some

structures, the genetic factors inﬂuencing change were

different from those inﬂuencing the volume itself,

suggesting the existence of genetic variants speciﬁc for

brain plasticity.

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History

ENIGMA was launched in December 2009 to help

‘break the logjam’ in genetic studies of the brain. At the

time, most neuroimaging genetics studies were assessing

historically candidate genetic variations, mostly in very

small samples of a few tens to hundreds of participants

(e.g., COMT , 5-HTTLPR, BDNF). These studies typically

reported ‘candidate gene’ effects that did not replicate

when tested in independent cohorts

19–21

. It became

apparent that very large numbers of genetic loci con-

tributed to variation in complex neurological or psychia-

tric traits, including imaging-derived brain measures—

each with a very small effect size—and only a few genetic

loci accounted for more than 1% of the variance in any

complex brain condition or measure

. Thus, scientists

began to recognize the need to pool multiple datasets

worldwide to perform better-powered studies of these

traits. In response, the ENIGMA Consortium’s initial plan

was to merge two ‘big data’ sources—neuroimaging and

genetics—with the aim of discovering the impact of

genetic factors on brain systems, to determine whether

these genetic factors underlie manifestation of disorders

within the brain, and to identify diagnostic and prognostic

neuroimaging biomarkers. A further goal was to improve

on previous literature-based meta-analyses by using har-

monized processing and analysis protocols on an unpre-

cedented scale. This was the impetus that launched

ENIGMA’s early studies.

In 2014, the NIH Big Data to Knowledge (BD2K) pro-

gram awarded a consortium grant to ENIGMA with seed

funding for WGs on nine disorders: SCZ, BD, MDD,

OCD, ADHD, ASD, SUD, 22q11DS, and the effects of the

human immunodeﬁciency virus (HIV) on the brain. This

support led to the largest neuroimaging studies for the

nine targeted disorders, with results reported in over 50

manuscripts. These initial successes provided the driving

force to establish an additional 21 disease WGs (see

Working Group chart, Fig. 2).

Following the model established by the Psychiatric

Genomics Consortium (PGC), which emphasized har-

monization of genomic analysis protocols across sites, the

ENIGMA Consortium created harmonized protocols to

analyze brain structure and function, along with genetic,

and clinical data across its WGs. Instead of centralizing

data, ENIGMA opted to work as a ‘distributed con-

sortium’, asking groups to run standardized protocols

themselves, rather than the approach used in the PGC,

where data are centralized. At the time, ENIGMA design

was important for the rapid acceptance of the consortium

in the ﬁeld, as it made contribution very easy; further, the

memoranda of understanding provided the basic guide-

lines for the trusted collaborative networks to develop. In

the meantime —with views on data sharing having chan-

ged quite considerably—many ENIGMA WGs now also

share (derived) individual data, allowing for more in-

depth analyses.

In ENIGMA’s genetic studies, many participating cen-

ters use different genotyping chips, so data were ﬁrst

imputed to common genomic references (such as the

Fig. 2 ENIGMA’s Working Group Flowchart. ENIGMA’s working

groups are divided into technical groups that work on testing

harmonized methods, and clinical groups that study different disorders

and conditions across psychiatry and neurology, as well as some

behaviors (e.g., schizotypy and antisocial behaviors). The use of

harmonized analysis methods across all the working groups has enabled

cross-disorder comparisons (e.g., in the affective/psychosis spectrum of

depression to bipolar disorder to schizophrenia), and transdiagnostic

analyses of risk factors such as childhood trauma across a number of

disorders (such as major depressive disorder (MDD) and post-traumatic

stress disorder (PTSD)). Several working groups, such as brain trauma and

anxiety, consist of several subgroups examining subtypes (e.g., panic

disorder or social anxiety), and allow analyses of overlap and differences

(e.g., between military and civilian brain trauma).

Thompson et al. Translational Psychiatry Page 5 of 26

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1000 Genomes reference panel), allowing each partici-

pating site to perform the same association tests between

brain measures and genetic variation at over 10 million

loci across the genome. Furthermore, the ENIGMA

Consortium standardized procedures for the extraction of

brain metrics (such as cortical thickness, cortical surface

area, and subcortical volume) from raw neuroimaging

data, implemented consensus protocols for data quality

control and outlier handling, and pioneered new

meta-analytic methods for the analysis of aggregated

statistical information (http://enigma.ini.usc.edu/

protocols/). ENIGMA’s meta-analyses estimated the size

and precision of the effects after pooling evidence from

multiple cohorts, and they also ranked the neuroimaging

effect sizes of ﬁndings emerging from case–control

comparisons, thereby setting the stage for deeper, sec-

ondary analyses aiming to explore potential moderators of

psychiatric and neurological disease. More recently, many

ENIGMA groups have moved beyond cohort level meta-

analyses to pooled, or ‘mega’-analyses (Using brain

volu-

metric data from ENIGMA’s OCD, ADHD, and ASD

working groups, Boedhoe et al.

compared meta-analysis

to mega-analyses that model site or cohort effects as

random effects, showing broad agreement. Mega-analyses

allow more sophisticated statistical adjustments as they

pool more information across cohorts; meta-analyses tend

to be more efﬁcient when ethical, legal or logistic con-

straints govern or restrict individual-level data transfer

(e.g., genome-wide genetic data).), where anonymized and

unidentiﬁable individual-level data are aggregated in a

central location, allowing more ﬂexible statistical designs,

such as machine learning analyses

, reliable estimation of

interaction effects, and examination of polygenic risk

scores. The type and amount of data transferred for each

analysis is chosen pragmatically for each study. Dis-

tributed analyses promote scientiﬁc engagement from

many groups worldwide and take advantage of distributed

computing resources that scale up as the network grows;

here the data transferred is mainly aggregate measures

such as quality control metrics and the statistical metrics

derived from agreed-upon analytical tests. On the other

hand, the centralized analyses are preferable when a

variable of interest is sparsely distributed across sites, (e.g.,

individuals with 22q11DS exhibiting psychotic symptoms)

or when a speciﬁc method is being developed, and com-

putational power or expertise is available at only a few

sites; here the data transferred usually include uni-

dentiﬁable derived imaging metrics (e.g., hippocampal

volume) and demographic or clinical information (age at

scan, sex, diagnostic status, etc.); however, this form of

analysis may limit participation and requires individual

data transfer agreements with participating sites. We note,

because of these required agreements with potentially

clinically sensitive patient information, and the project-

speciﬁc design of the ‘centralized’ approaches, ENIGMA

does not curate a database for repeated or open access,

and each cohort PI approves of each project for which

they contribute data.

ENIGMA’s genetic studies

Uncovering the genetic basis of brain morphometric

variation

The ﬁrst demonstration of the value of the ENIGMA

approach was the identiﬁcation of genetic loci associated

with variation in subcortical volumes including the cau-

date, putamen, and hippocampus (see Fig. 3)

14,24,25

. These

genome-wide association studies (GWAS) yielded intri-

guing new leads regarding the genetic architecture of the

human brain that were only possible because ENIGMA

afforded increased power to detect subtle effects. More

recently, ENIGMA identiﬁed more than 200 individual

loci that signiﬁcantly contribute to variation in brain

measures, with p-values reaching 10

−180

; each single locus

accounted for only 0.1–1% of phenotypic variance, but up

to 20% of the variance in aggregate. For this effort

ENIGMA had partnered with the CHARGE Consortium

and UK Biobank on a series of studies of 70 cortical

measures, including regional cortical thickness and sur-

face area

. These discoveries resulted in an annotated

atlas of common genetic variants that contribute to

shaping the human cerebral cortex. Of particular interest,

we found that genetic loci affecting brain morphology

show enrichment for developmentally regulated genes

and human-speciﬁc regulatory elements

26,27

. Ongoing

efforts are beginning to map these genetic effects at a

ﬁner-grained spatial resolution using shape analysis, sur-

face- and voxel-based analyses

28–31

. Moving beyond the

mass univariate methods, which analyze each brain

measure separately, ENIGMA has begun to use multi-

variate methods to meet the challenge of quantifying the

complex relationships between brain networks—or ‘con-

nectomes’—and the genome

32–34

Current ENIGMA sample sizes (which now exceed

50,000) are sufﬁciently large to identify genetic associa-

tions at a pace comparable to that of GWAS for other

phenotypes. In a recent analysis, Holland

contrasted

rates of discovery of genetic loci by ENIGMA and the

PGC and noted the distribution of effect sizes for some

brain measures (e.g., putamen volume) may indeed be

enriched for slightly larger effects compared to behavioral

traits (see also Le and Stein

and Franke et al.

). Still, a

central understanding gained from the ENIGMA asso-

ciation screens is that neuroimaging genetics studies—just

like analyses of behavioral measures, require tens (perhaps

hundreds) of thousands of participants to obtain robust

and reproducible effects of common polymorphisms.

Most individual effect sizes are very small explaining

<0.2% of variance, as for other complex human traits.

Thompson et al. Translational Psychiatry Page 6 of 26

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GWAS of multiple imaging measures may offer a way to

parcellate the brain into clusters or sectors with over-

lapping genetic drivers, perhaps boosting the power to

discover genetic loci, by aggregating regions based on

their genetic correlation.

Uncovering the genetic basis of brain change

The quest to discover genetic loci that modulate brain

development and aging led to the launch of the ENIGMA-

Plasticity WG

, which uses longitudinal brain imaging

data from 36 cohorts worldwide to estimate rates of brain

growth or atrophy, and performs GWAS to ﬁnd genetic

markers that may inﬂuence these rates of change. The

ENIGMA-Plasticity WG has established the heritability of

brain changes over time and has shown that distinct

genetic factors inﬂuence regional brain volumes and their

rate of change, implying the existence of genetic variants

speciﬁcally associated with change

. The WG is further

investigating how closely developmental and aging-related

genes overlap, and how they overlap with genetic loci that

Fig. 3 Genetic Inﬂuences on brain structure: effects of common and rare genetic variants. ENIGMA’s large-scale genetic analyses study the

effects of both common and rare genetic variants on brain measures. a A series of progressively larger genome-wide association studies have

revealed over 45 genetic loci associated with subcortical structure volumes (Hibar

, Satizabal

) and over 200 genetic loci associated with cortical

thickness and surface area Grasby

. The Manhattan plots here (adapted from Hibar

, show the genome (on the x-axis) and the evidence for

association (as a logarithm of the p-value, on the y-axis) for each common genetic variant (or SNP) with the volume of each brain structure shown.

b Genetics of Hippocampal Volume. A subsequent genome-wide association study (GWAS) of 33,536 individuals discovered six independent loci

signiﬁcantly associated with hippocampal volume, four of them novel. Of the novel loci, two lie within key genes involved in neuronal migration and

microtubule assembly (ASTN2 and MAST4) (Hibar 2017). An interactive browser, ENIGMA-Vis—http://enigma-brain.org/enigmavis—can be used to

navigate ENIGMA’s genomic data. Initially started as a web page to plot ENIGMA summary statistics data for a speciﬁc genomic region, ENIGMA-Vis

grew over the years into a portal with tools to query, visualize, and navigate the effects, and relate them to other GWAS (Shatokhina

197

). c In

complementary work on rare variants by the ENIGMA-CNV Working Group, Sønderby and colleagues (2018) examined effects of the 16p11.2 distal

CNV that predisposes to psychiatric conditions including autism spectrum disorder and schizophrenia. ENIGMA (including the 16p11.2 European

Consortium) and deCODE datasets were combined to discover negative dose-response associations with copy number on intracranial volume and

regional caudate, pallidum and putamen volumes—suggesting a neuropathological pattern that may underlie the neurodevelopmental syndromes.

The agreement across datasets is apparent in the Forest plots for each brain region. [Data adapted, with permission from the authors and publishers].

Thompson et al. Translational Psychiatry Page 7 of 26

UNCORRECTED PROOF

are associated with risk for development of psychiatric

and neurological disease throughout life. Overall, the high

rate of discovery driven by ENIGMA is offering initial

glimpses of the overlap among genetic drivers of brain

change throughout life with speciﬁc markers of brain

structure and function.

Uncovering the genetic basis of brain functional variation

The ENIGMA Consortium has also carried out genetic

association studies of EEG-derived phenotypes. The ﬁrst

study

of the EEG WG performed the largest GWAS to

date of oscillatory power across a range of frequencies

(delta 1–3.75 Hz, theta 4–7.75 Hz, alpha 8–12.75 Hz, and

beta 13–30 Hz) in 8425 healthy subjects. They identiﬁed

several novel genetic variants associated with alpha

oscillatory brain activity that were previously linked to

psychiatric disorders.

Characterizing the association between brain morphology

and disease-risk genes

In an early ENIGMA study, minimal overlap was detected

between schizophrenia-relate d and brain-related genetic

loci

. These questions were revisited with Bayesian mod-

els

and LD-score regression methods

which identiﬁed

stronger overlap between genetic loci involved in cortical

structure and loci implicated in insomnia, major depression,

Parkinson’s disease, and general cognitive ability or IQ

Despite initial negative results

,ENIGMA’sgrowingsam-

ple size led to more powerful results, allowing for the recent

successes in the discovery of brain-related genetic variants

that also affect risk for schizophrenia

43,44

,OCD

,anxiety

disorders

,PTSD

,ADHD

,anorexianervosa

, Tourette

syndrome

,andinsomnia

As the sample size of brain scans in the ENIGMA Con-

sortium increa sed beyond 50,000 MRI scans, it became

possible to discover further genetic loci associated with

multiple brain traits implicated in brain disorders. A recent

example is an ENIGMA-CHARGE GWAS of white matter

(WM) hyperintensities, a sign of vascular brain disease, by

Mather et al. (in prep), which found heterogeneous effects

for variants associated with lesions near the ventricles ver-

sus lesions elsewhere in the brain. An innovative feature of

this analysis was the use of anatomical clustering of traits to

yield more powerful brain GWAS results. Anatomical or

genetic clustering is yet another methodological improve-

ment implemented by ENIGMA, that can be used widely to

enhance detection of genetic associations in multiple brain

disorders (see Lorenzi, Couvy-Duchesne for other multi-

variate imaging GWAS approaches

50,51

Uncovering the epigenetic basis of brain morphometric

variation

Inspired by these successes, ENIGMA widened the

scope of its WGs to embrace the study of epigenetic

variations. ENIGMA’s Epigenetics group has already

identiﬁed two sites in the genome where methylation

relates to hippocampal volume (N = 3337)

. Ongoing

studies focus on brain measures sensitive to epigenetic

age, an index of biological as opposed to chronological

aging, in both health and disease.

From common nucleotide variations to rare copy number

variants (CNV)

The ENIGMA-CNV WG was launched to study the

effects of CNVs, relatively rare genetic variants predis-

posing individuals to various neuropsychiatric disorders.

The ENIGMA collaborative approach is ideal for studying

low-frequency variants, as such efforts require large

samples that are usually beyond the scope of a single

study. Their ﬁrst reports were on the 16p11.2 distal

and

15q11.295 CNVs (Fig. 3) and additional studies on other

CNVs are underway.

ENIGMA disorder-based neuroimaging studies

ENIGMA-schizophrenia

The Schizophrenia WG was formed in 2012, and has

since analyzed data from 39 cohorts worldwide and has

identiﬁed case–control differences in brain morpho-

metry

1,54,55

and WM microstructure

56,57

, on an unpre-

cedented scale. ENIGMA-Schizophrenia was the ﬁrst

working group to publish large-scale analyses of disease,

in two seminal papers on case–control differences in

brain morphometry based on the largest samples to date.

Van Erp and ENIGMA colleagues

ﬁrst reported that

patients with SCZ (N = 2028 patients) had smaller hip-

pocampus (Cohen’s d = −0.46), amygdala (d = −0.31),

thalamus (d = −0.31), nucleus accumbens (d = −0.25),

total intracranial volumes (d = −0.12), and larger palli-

dum (d = 0.21) and lateral ventricle volumes (d = 0.37)

compared to healthy controls (N = 2540). In a subsequent

study, the team1 expanded their sample to include 4474

individuals with SCZ and 5,098 controls to study cortical

structures. Compared to healthy controls, patients with

SCZ had globally thinner cortices (left/right hemisphere:

d = −0.53/−0.52) and smaller overall cortical surface area

(left/right hemisphere: d = −0.25/−0.25), with greatest

effect sizes in frontal and temporal regions.

Figures 4 and 5 present these cortical and subcortical

ﬁndings alongside data from several other disorders. It is

notable that these ﬁndings from ENIGMA

13,54

were

replicated in a large independent study by the Japanese

COCORO Consortium

, and a recent Norwegian study

of 16 cohorts by Alnæs et al.

. The convergence of all

three studies, reviewed in Kochunov et al.

, represents a

new level of rigor and reproducibility in a ﬁeld where the

existence of morphometric correlates of schizophrenia

was once hotly debated

Thompson et al. Translational Psychiatry Page 8 of 26

UNCORRECTED PROOF

Brain alterations were also discovered in relation to

clinical features of the disease. In follow-up analyses,

Walton et al. found that positive symptom severity was

negatively related to the thickness of the superior tem-

poral gyrus bilaterally

, while the severity of negative

symptoms was negatively related to the cortical thickness

of several prefrontal regions and particularly the left

medial orbitofrontal cortex (MOFC)

At this point it is worth considering the added value of

other data modalities, such as diffusion MRI, which offers

complementary information on microstructural abnorm-

alities, especially in the WM, that are not detectable on

standard anatomical MRI. ENIGMA’s Diffusion MRI

working group, launched in 2012 with protocols for dif-

fusion tensor imaging (DTI), published a series of papers

on the heritability and reproducibility of DTI measures

derived with a protocol based on tract-based spatial sta-

tistics

64–66

. Over ten of ENIGMA’s working groups have

since used this protocol to rank effect sizes for DTI

metrics across key WM tracts.

Kelly et al. reported on widespread WM abnormalities

in schizophrenia, pooling data from 2359 healthy controls

and 1963 patients with SCZ from 29 independent inter-

national studies

. Signiﬁcant reductions in fractional

anisotropy (FA) in patients with SCZ were widespread

across major WM fasciculi. While effect sizes varied by

tract and included signiﬁcant reductions in the anterior

corona radiata (d = 0.40) and corpus callosum (d = 0.39,

speciﬁcally its body (d = 0.39) and genu (d = 0.37)),

effects were observed throughout the brain, with peak

reductions observed for the entire WM skeleton (d =

0.42). Figure 6 shows these ﬁndings alongside data from

two other disorders for which ENIGMA published large-

scale DTI analyses, MDD

, and 22q11DS

ENIGMA-BD

Formed shortly after the Schizophrenia WG, and fol-

lowing similar protocols, the ENIGMA’s BD WG reported

on cortical thickness and surface area measures using

anatomical MRI data from 1837 adults with BD and 2582

healthy controls, from 28 international groups

. BD was

associated with reduced cortical thickness in bilateral

frontal, temporal and parietal regions, and particularly in

the left pars opercularis (d = −0.29), the left fusiform

Fig. 4 ENIGMA’s large-scale studies of nine brain disorders. Cortical gray matter thickness abnormalities as Cohen’s d, are mapped for nine

different disorders, for which worldwide data were analyzed with the same harmonized methods. Although the cohorts included in the studies

differed, as did the scanning sites and age ranges studied, some common and distinct patterns are apparent. Cortical maps for major depressive

disorder (MDD), bipolar disorder (BD) and schizophrenia show gradually more extensive proﬁles of deﬁcits. Across all disorders, the less prevalent

disorders tend to show greater effects in the brain: the relatively subtle pattern of hippocampal-limbic deﬁcits in MDD broadens to include frontal

deﬁcits in bipolar disorder (consistent with frontal lobe dysfunction and impaired self-control). In schizophrenia, deﬁcits widen to include almost the

entire cortex—only the primary visual cortex (speciﬁcally the calcarine cortex) failed to show thickness alterations in patients, after meta-analysis.

Autism spectrum disorder (ASD) and the 22q deletion syndrome (22q11DS)—a risk condition for ASD—are associated with hypertrophy in frontal

brain regions, while patients with obsessive-compulsive disorder (OCD) and alcohol use disorder tend to show deﬁcits in frontal brain regions

involved in self-control and inhibition. More reﬁned analyses are now relating symptom domains to these and other brain metrics, within and across

these and other disorders.

Thompson et al. Translational Psychiatry Page 9 of 26

UNCORRECTED PROOF

gyrus (d = −0.29), and left rostral middle frontal cortex

(d = −0.28). Interestingly, lithium use was associated with

thicker cortex in several areas. The WG also examined

case–control differences in subcortical volumes in 1710

patients with BD and 2594 healthy controls; they found

that BD was associated with reductions in the volume of

the hippocampus (d = −0.23) and the thalamus (d =

−0.15), and with enlarged lateral ventricular volume (d =

0.26). A follow-up study, showed that when applied to

regional cortical thickness, surface area, and subcortical

volumes, machine learning methods (based on support

vector machines) differentiated BD participants from

controls with above chance accuracy even in a large and

heterogeneous sample of 3020 participants from 13

ENIGMA cohorts worldwide

. Aggregate analyses of

individual subject data yielded better performance than

meta-analysis of site-level results. Age and exposure to

anticonvulsants were associated with greater odds of

correct classiﬁcation. Although short of the 80% clinically

relevant threshold, the 65.2% accuracy (0.71 ROC-AUC)

is promising, as the study focused on a difﬁcult to diag-

nose, highly heterogeneous condition and used only

Fig. 5 Subcortical abnormalities in schizophrenia, bipolar

disorder, major depressive disorder, and ADHD. a ENIGMA’s

publications of the three largest neuroimaging papers on

schizophrenia (SCZ), bipolar disorder (BD), and major depressive

disorder (MDD), suggested widespread cross-disorder differences in

effects (van Erp 2015, Hibar

, Schmaal 2015). By processing 21,199

people’s brain MRI scans consistently, we found greater brain

structural abnormalities in SCZ and BD versus MDD, and a very

different pattern in attention-deﬁcit/hyperactivity disorder (ADHD;

Hoogman

). Subcortically, all three disorders involve hippocampal

volume deﬁcits—greatest in SCZ, least in MDD, and intermediate in

BD. As a slightly simpliﬁed ‘rule of thumb’, the hippocampus,

ventricles, thalamus, amygdala and nucleus accumbens show volume

reductions in MDD that are around half the magnitude of those seen

in BD, which in turn are about half the magnitude of those seen in

SCZ. The basal ganglia are an exception to this rule—perhaps because

some antipsychotic treatments have hypertrophic effects on the basal

ganglia, leading to volume excesses in medicated patients. In ADHD,

however, the amygdala, caudate and putamen, and nucleus

accumbens all show deﬁcits, as does ICV (ventricular data is not

included here for ADHD, as it was not measured in the ADHD study).

A web portal, the ENIGMA Viewer, provides access to these summary

statistics from ENIGMA’s published studies of psychiatric and

neurological disorders (http://enigma-viewer.org/About_the_projects.

html; Zhang

198

). b Independent work by the Japanese Consortium,

COCORO, found a very similar set of effect sizes for group differences

in subcortical volumes between schizophrenia patients and matched

controls.

Fig. 6 White matter microstructure in schizophrenia, major

depressive disorder, and 22q11.2 deletion syndrome. a White

matter microstructural abnormalities are shown, by tract, based on the

largest-ever diffusion MRI studies of these three disorders. In

schizophrenia (SCZ), fractional anisotropy, a measure of white matter

microstructure, is lower in almost all individual regions, and in the full

skeleton. In major depressive disorder (MDD), a weak pattern of effects

is observed, again with MDD patients showing on average lower FA

across the full white matter skeleton, when compared to controls. In

comparisons between 22q11.2 deletion syndrome (22q11DS) and

matched controls, by contrast, the average FA along the full white

matter skeleton does not show systematic differences; instead, while

some regions do show on average lower FA in affected individuals

compared with controls, several white matter regions show higher FA.

b Relative to appropriately matched groups of healthy controls (HC),

group differences in fractional anisotropy are shown for ENIGMA’s

studies of SCZ, MDD (both in adults), and 22q11.2 deletion syndrome.

[Data adapted, with permission of the authors and publishers, from

Kelly

, van Velzen

, and Villalon (2019); a key to the tract names

appears in the original papers; some tracts (i.e. the hippocampal

portion of the cingulum) were omitted from the 22q11DS analysis as

they were not consistently in the ﬁeld of view for some cohorts of the

working group].

Thompson et al. Translational Psychiatry Page 10 of 26

UNCORRECTED PROOF

engineered features, not raw brain imaging data. ENIG-

MA’s multi-site design may also offer a more realistic

assessment of “real-world” accuracy, by repeatedly leaving

out different sites’ data for cross-validation. Future mul-

tisite brain-imaging machine learning studies will begin to

move towards sharing of more detailed individual subject

data, not only a selection of discrete features or site-level

results derived from a single modality; unsupervised

machine learning techniques may offer potential to better

understand the heterogeneity in the disorder. The

ENIGMA-BD DTI WG conducted both a mega- and

meta-analysis of 3,033

subjects (1482 BD and 1551 con-

trols)

. Both analyses found lower FA in patients with BD

compared with healthy controls in most brain regions,

with the highest effect sizes in the corpus callosum and

cingulum.

ENIGMA-MDD

Brain morphometric analyses conducted by the

ENIGMA-MDD WG were based on MRI data from 1728

patients with MDD and 7199 controls for subcortical

volumes

and from 2148 patients with MDD and 7957

controls for cortical measures

. These studies found that

patients with MDD had lower hippocampal volumes (d =

−0.14), an effect driven by patients with recurrent illness

(d = −0.17) and by patients with an adolescent (≤21

years) age of onset (d = −0.20). First-episode patients

showed no subcortical volume differences compared to

controls. Adult patients (>21 years) had reduced cortical

thickness in bilateral orbitofrontal cortex (OFC), anterior

and posterior cingulate cortex, insula, and temporal lobe

regions (d’s: −0.10 to −0.14). In contrast, adolescent

patients showed no differences in cortical thickness but

showed lower total surface area, which seemed to be

especially driven by lower surface area in frontal (medial

OFC and superior frontal gyrus), visual, somatosensory,

and motor areas (d = −0.26 to −0.57). Moreover, these

differences in gray matter morphometry observed in

MDD do not involve abnormal asymmetry, as shown in a

joint study by the Laterality and the MDD WGs involving

2540 MDD individuals and 4230 controls, from 32

datasets

A follow-up analysis on a subset of these aforemen-

tioned data found that the brain MRIs of adult patients

with MDD (18–75 years old) appeared, on average, 1.08

years older than those of controls (d = 0.14)

. This ‘brain

age’ estimate was based on a machine learning algorithm

trained to predict chronological age from morphometric

data from 2188 controls across 19 cohorts and subse-

quently applied to hold-out data from 2126 healthy con-

trols and 2675 people with MDD. The largest brain aging

effects were observed in antidepressant users (+1.4 years;

d = 0.15), currently depressed (+1.5 years; d = 0.18), and

remitted patients (+2.2 years; d = 0.18), compared to

controls. Within ENIGMA-MDD, Opel et al. also studied

the effects of obesity on structural brain metrics of

patients and controls (N = 6420)

. Obesity effects were

not different between patients and controls, but there was

a signiﬁcant obesity by age interaction in relation to

cortical thickness, with thinner cortices in older obese

individuals. Cortical thickness deﬁcits related to obesity

were strongest in the temporal and frontal cortical

regions, and overlapped with patterns observed in several

neuropsychiatric disorders, but exceeded those found in

MDD without regard for BMI —in terms of the effect

sizes and range of structures affected. The magnitude of

these effects suggests a need to better understand the

connections between BMI, brain aging and mental health.

Capitalizing on the statistical power of ENIGMA to

examine the role of risk factors, Frodl

and Tozzi

examined the association between retrospectively assessed

childhood maltreatment (including emotional, physical

and sexual abuse, or emotional and physical neglect), and

brain morphometry in 3036 and 3872 individuals (aged

13–89) with and without MDD, respectively. Greater

exposure to childhood maltreatment was associated with

lower cortical thickness of the banks of the superior

temporal sulcus and supramarginal gyrus, and with lower

surface area across the whole brain and in the middle

temporal gyrus. Sex differences were also observed: in

females, greater maltreatment severity was associated with

overall lower gray matter thickness and smaller caudate

volumes, whereas in males, greater maltreatment severity

was associated with lower thickness of the rostral anterior

cingulate cortex.

In addition to these investigations of gray matter in

MDD, a large-scale analysis of WM microstructure with

DTI has also been completed, comparing 1305 adults and

adolescents with MDD to 1602 healthy controls from

20 samples worldwide

. In adults with MDD, widespread

lower FA values were found in 16 out of 25 WM tracts of

interest (d’s = 0.12–0.26), with the largest differences in

the corpus callosum and corona radiata. Widespread

increased radial diffusivity (RD) was also observed (d’s =

0.12–0.18) and was driven by patients with recurrent

MDD and an adult-onset of depression.

ENIGMA-PGC post-traumatic stress disorder

In partnership with the PGC, ENIGMA launched a WG

on PTSD that has analyzed neuroimaging and clinical

data from 1868 individuals (including 794 patients with

PTSD) from 16 cohorts. In this ﬁrst ENIGMA-PTSD

study, Logue and colleagues found that patients with

current PTSD had smaller hippocampal volumes (d =

−0.17) compared to trauma-exposed controls

. Child-

hood trauma predicted smaller hippocampal volume (d =

−0.17) independent of diagnosis. In a subsequent study,

the WG found that cortical thickness in 3378 individuals

Thompson et al. Translational Psychiatry Page 11 of 26

UNCORRECTED PROOF

(including 1309 patients with PTSD) was lower in PTSD

in the orbitofrontal cortex, cingulate cortex, precuneus,

insula, and lateral parietal cortices. In addition, a DTI

meta-analysis of 3057 individuals (including 1405 patients

with PTSD) from 25 cohorts found alterations in WM

organization in the tapetum, a structure that connects the

left and right hippocampus

. Structural covariance net-

work analysis applied to data from 3505 individuals

(including 1344 patients with PTSD), which examined

correlated patterns of cortical thickness and surface area,

found that PTSD is associated with network centrality

features of the insula and visual association areas

.To

extend these ﬁndings, ongoing studies are assessing cor-

tical structure

78,79

and hippocampal subﬁelds in PTSD

and MDD

80–83

, to better understand the pattern and

regional speciﬁcity of hippocampal deﬁcits in the two

disorders, and whether these patterns coincide.

ENIGMA-addictions/SUD

The ENIGMA-Addictions/SUDs WG has 33 partici-

pating sites, contributing MRI data from 12,347 indivi-

duals of whom 2277 are adult patients with SUD relating

to one of ﬁve substances (alcohol, nicotine, cocaine,

methamphetamine, or cannabis)

5,84,85

. In these data,

Mackey

observed lower cortical thickness/subcortical

volume in cases relative to controls in regions that play

key roles in evaluating reward (MOFC, amygdala), task

monitoring (superior frontal cortex), attention (superior

parietal cortex, posterior cingulate) and perception/reg-

ulation of internal body states (insula). While the most

pervasive case–control differences appeared to be related

to alcohol dependence, some effects were observed for

substance dependence generally (e.g., the insula and

MOFC). A support vector machine trained on cortical

thickness and subcortical volume successfully classiﬁed

set-aside test sets for both alcohol (ROC-AUC: 0.74–0.78;

p < 0.0001) and nicotine dependence (ROC-AUC:

0.60–0.64; p < 0.0001), relative to non-dependent con-

trols

. A separate meta-analysis also compared the effect

size of addiction-related brain impairment to that of other

psychiatric disorders: effect sizes of alcohol-related brain

differences in subcortical brain regions were equivalent to

those reported for schizophrenia

ENIGMA-obsessive-compulsive disorder

The ENIGMA’s OCD WG grew out of a previously

established consortium (the OCD Brain Imaging Con-

sortium, or OBIC)

, and has published the largest studies

to date of brain structure in adult and pediatric OCD,

using both meta- and mega-analytic approaches

6,88

. The

ﬁrst study analyzed MRI scans from 1830 patients diag-

nosed with OCD and 1759 controls across 35 cohorts

from 26 sites worldwide

. Unmedicated pediatric OCD

patients demonstrated larger thalamic volumes, while the

pallidum was enlarged in adult OCD patients with disease

onset at childhood. Adult OCD patients also had sig-

niﬁcantly smaller hippocampal volumes (d = −0.13), with

stronger effects in medicated patients with adult-onset

OCD compared to healthy controls (d = −0.29). A cor-

tical study included data from 1905 patients diagnosed

with OCD and 1760 healthy controls across 38 cohorts

from 27 sites worldwide. In adult patients diagnosed with

OCD versus controls, signiﬁcantly smaller surface area of

the transverse temporal cortex (d = −0.16) and a thinner

inferior parietal cortex (d = − 0.14) were found. Medi-

cated adult patients with OCD also showed thinner cor-

tices throughout the brain (Cohen’s d effect sizes varied

between −0.10 and −0.26). Pediatric patients with OCD

showed signiﬁcantly thinner inferior and superior parietal

cortices (d’s = −0.24 to −0.31), but none of the regions

analyzed showed signiﬁcant differences in cortical surface

area. However, medicated pediatric patients with OCD

had smaller surface area in frontal regions (d’s = −0.27 to

−0.33), that may indicate a delayed cortical maturation.

The absence of cortical surface area abnormalities in adult

patients with a childhood onset of OCD could indicate a

normalization of these abnormalities—a hypothesis that is

now being explored with longitudinal data collection.

To assess whether the anatomical differences could be

used to create a neuroimaging biomarker for OCD, a

machine learning analysis of the cortical and subcortical

data was performed with 2304 OCD patients and 2068

controls. Classiﬁcation performance across ten different

machine and deep learning approaches was poor. With

site-stratiﬁed cross-validation, the ROC-AUC ranged

between 0.57 and 0.62. The performance dropped to

chance level when leave-one-site-out cross-validation was

used, with classiﬁcation performance between 0.51 and

0.54. This indicates that these anatomical brain features

do not provide a biomarker for OCD. But when patients

were stratiﬁed according to whether they had used med-

ication, classiﬁcation performance improved remarkably.

Medicated OCD patients and controls could then be

distinguished with 0.73, unmedicated OCD and controls

with 0.61, and medicated and unmedicated OCD patients

with 0.86 ROC-AUC. These multivariate results therefore

mirror the univariate results, and highlight that medica-

tion use is associated with large differences in brain

anatomy

The OCD WG, in conjunction with the Laterality WG,

studied brain asymmetry in OCD using 16 pediatric

datasets (501 patients with OCD and 439 healthy con-

trols), and 30 adult datasets (1777 patients and 1654

controls)

. In the pediatric datasets, the largest

case–control differences were observed for volume

asymmetry of the thalamus (more leftward in patients

compared to controls; d = 0.19) and the pallidum (less

leftward in patients compared to controls; d = −0.21). No

Thompson et al. Translational Psychiatry Page 12 of 26

UNCORRECTED PROOF

asymmetry differences were found in the adult datasets.

These ﬁndings may reﬂect altered neurodevelopmental

processes in OCD, affecting cortico-striato-thalamo-

cortical circuitry, which is involved in a wide range of

cognitive, motivational and emotional processes.

ENIGMA-attention-deﬁcit/hyperactivity disorder

ENIGMA’s ADHD WG has analyzed data from up to

2264 participants with ADHD and 1934 controls from up

to 36 sites (age range: 4–63 years; 66% males)

. Volumes

of the nucleus accumbens (d = −0.15), amygdala (d =

−0.19), caudate (d = −0.11), hippocampus (d = −0.11),

putamen (d = −0.14), and ICV (d = −0.10) were smaller

in cases relative to controls. Effect sizes were highest in

children. No statistically signiﬁcant univariate

case–control differences were detected in adults. Volume

differences were found to have similar effect sizes in those

treated with psychostimulant medication and those naïve

to psychostimulants. Bioinformatics analyses suggested

that the selective subcortical brain region vulnerability

was associated with differential expression of oxidative

stress, neurodevelopment and autophagy pathways

The ENIGMA-ADHD WG was the ﬁrst WG in

ENIGMA to perform a detailed investigation of the case/

control effects on the cerebellum. Differential age trajec-

tories were identiﬁed for children with ADHD when

compared with typically developing children for the cor-

pus medullare

In an analysis of the cerebral cortex, lower surface area

values were found, on average, in children with ADHD,

mainly in frontal, cingulate, and temporal regions; the

largest effect was for total surface area (d = − 0.21).

Fusiform gyrus and temporal pole cortical thickness were

also lower in children with ADHD. All effects were most

pronounced in early childhood. Neither surface area nor

thickness differences were found in the adolescent or

adult groups

, but machine learning analyses supported

the hypothesis that the case–control differences observed

in childhood could be detected in adulthood

. Impor-

tantly, many of the same surface area features were

associated with subclinical ADHD symptoms in children

from the general population that do not have a clinical

psychiatric diagnosis. Several of the observed brain

alterations fulﬁlled many of the criteria of ‘endopheno-

types’ (An endophenotype is a trait, such as brain struc-

ture or function, related to the biological process of a

disorder; to qualify as an endophenotype, the trait, should

be heritable, co-segregate with an illness, yet be present

even when the disease is not, and be found in non-affected

family members at a higher rate than in the general

population

95,96

), as they were also seen in unaffected

siblings of people with ADHD in a subsample analysis of

the cortical features. The stronger effects in children may

reﬂect a developmental delay, perhaps due in part to

genetic risk factors, given recent ﬁndings of overlap

between the genetic contributions to ADHD and to sub-

cortical volumes

13,47

ENIGMA-autism spectrum disorders

The ENIGMA-ASD WG published the largest neuroi-

maging study of autism analyzing data from 1571 parti-

cipants with ASD and 1651 controls, from 49 sites

worldwide (ages 2–64 years)

. Unlike most of the dis-

orders discussed so far, the direction of effects seen in

ASD varied by brain region, and did so across the age span

analyzed. ASD was associated with larger lateral ventricle

and intracranial volumes, greater frontal cortical thickness

and lower temporal cortical thickness (d = − 0.21 to 0.20).

Participants with ASD also had, on average, lower sub-

cortical volumes for the pallidum, putamen, amygdala,

and nucleus accumbens. Post hoc fractional polynomial

analyses showed a sharp increase in volumes in the same

regions in childhood, peaking in adolescence and

decreasing again in adulthood. Overall, patients with ASD

showed altered morphometry in the cognitive and affec-

tive associated-regions of the striatum, frontal cortex, and

temporal cortex.

The ASD group worked together with the Laterality

group to produce the largest ever study of brain asym-

metry in ASD, involving 1774 patients and 1809 controls,

from 54 datasets

. Generally, subtle but widespread

reductions of cortical thickness asymmetries were present

in patients with ASD compared to controls, as well as

volume asymmetry of the putamen, and surface area

asymmetry of the MOFC (the strongest effect had

Cohen’s d = −0.16). Altered lateralized neurodevelop-

ment may, therefore, be a feature of ASD, affecting

widespread cortical regions with diverse functions.

Neurogenetic disorders, CNV, and rare

neurodevelopmental conditions

Several neurodevelopmental disorders arise due to the

abnormal duplication or deletion of segments of the

genome. ENIGMA has dedicated WGs studying 22q11DS,

Gaucher’s disease, and Hepatic Glycogen storage dis-

ease

98,99

, along with a CNV WG meta-analyzing imaging

data from carriers of several other CNVs

53,100

. Here, we

focus on the work of the two most established groups,

that examine carriers of 22q11.2 deletions and

other CNVs.

ENIGMA- 22q11.2 deletion syndrome

22q11DS is associated with a 20-fold increased risk for

psychosis, and an elevated risk for developmental neu-

ropsychiatric disorders such as ASD. 22q11DS provides a

‘genetics-ﬁrst’ framework to study the brain markers

underlying complex psychiatric phenotypes. The

ENIGMA-22q11DS working group analyzed the largest

Thompson et al. Translational Psychiatry Page 13 of 26

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dataset to date of brain images from patients with

22q11DS from 10 cohorts including 466 individuals with

22q11DS and 374 matched controls. Compared to con-

trols, 22q11DS individuals showed overall thicker cortical

gray matter (left/right hemispheres: Cohen’s d = 0.61/

0.65), but pervasive reductions in cortical area (left/right

hemispheres: d = −1.01/−1.02), with speciﬁc anatomic

patterns. Machine learning methods were applied to the

cortical thickness and area measures to achieve a high

accuracy (sensitivity 94.2%; speciﬁcity 93.3%) in classifying

22q11DS cases and controls

. ENIGMA subcortical

shape analysis pipelines also identiﬁed complex structural

differences across many subcortical structures between

individuals with 22q11DS and controls

101

. Analysis of

diffusion MRI from the same subjects (N = 594) revealed

abnormalities in the corpus callosum, superior long-

itudinal fasciculus, and corona radiata

. Ongoing work

uses more advanced imaging protocols

—including

‘multishell’ diffusion protocols that allow for the estima-

tion of biophysical compartments in the tissue—to test

hypotheses about speciﬁc cellular processes and speciﬁc

ﬁber tracts that may be especially vulnerable in 22q11DS

(e.g., the corpus callosum), as well as ﬁber tracts that

appear to be relatively spared (e.g., the cortico-fugal

tracts

ENIGMA-copy number variations

This WG was set up to examine the effect of rare CNVs,

risk factors for a variety of neuropsychiatric disorders, on

brain structure. Due to their low prevalence

102,103

, their

effects on the brain have been hard to establish. Sønderby

and colleagues focused on the 16p11.2 distal CNV that

predisposes to psychiatric conditions including ASD and

schizophrenia. ENIGMA (including the 16p11.2 European

Consortium) and deCODE datasets were combined to

compare subcortical brain volumes of carriers of 15

16p11.2 distal deletion and 18 duplication to 7714 non-

carriers which led to the discovery of negative dose-

response associations with copy number on intracranial

volume and regional accumbens, caudate, pallidum and

putamen volumes—suggesting a neuropathological pat-

tern that may underlie the neurodevelopmental syn-

dromes

. A further study

100

including the UK Biobank

assessed the association of the 15q11.2 CNV with cogni-

tion and cortical and subcortical morphology in more

than 45,000 individuals from 38 datasets (203 individuals

with a 15q11.2 deletion, 45,247 non-carriers, and 306

duplication carriers). The authors found a clear pattern of

widespread poorer cognitive performance, smaller surface

area and thicker cortices for deletion carriers compared to

non-carriers and duplication carriers, particularly across

the frontal lobe, anterior cingulate and pre/postcentral

gyri. The pattern of results ﬁts well with known molecular

functions of the genes in the 15q11 region and suggests

involvement of these genes in neuronal plasticity and

cortical development. Thus, the results from ENIGMA-

CNV have shown that several CNVs cause abnormal brain

patterns and inform on genetically determined variation

in brain development and their relation to neurodeve-

lopmental disorders. Additional studies on other CNVs

are in progress.

Newly established working groups

In the last two years, seven additional ENIGMA WGs

have formed to study speciﬁc disorders and important

transdiagnostic conditions: anxiety disorders, suicidal

thoughts and behavior, sleep and insomnia, eating dis-

orders (including bulimia and anorexia nervosa sub-

groups

104

), irritability, antisocial behavior, and

dissociative identity disorder. The starting point of the

anxiety group was an international voxel-based morpho-

metry mega-analysis on social anxiety disorder

105

, sup-

ported by ﬁndings demonstrating that structural brain

alterations related to social anxiety run in families

106

.At

present, the anxiety WG has four subgroups including

over 5000 patients: besides social anxiety disorder (1250

patients)

107

, there are groups devoted to generalized

anxiety disorder (1329 patients), panic disorder (1300

patients), and speciﬁc phobia (1224 patients), allowing for

disorder-speciﬁc and cross-disorder comparisons. The

antisocial behavior WG aims to clarify how conduct dis-

order, psychopathy, and antisocial personality disorder

relate to differences in brain structure, function, and

connectivity. Its goals include examination of different

phenotypes (e.g., reactive vs proactive aggression),

population-based samples with dimensional measures of

antisocial behavior, and genetic data from case–control

and population-based studies.

Building on the promising ﬁndings from the psychiatric

WGs, ENIGMA established seven WGs studying speciﬁc

conditions in neurology and cancer-related cognitive

impairment: epilepsy, traumatic brain injury, Parkinson’s

disease, neuro-HIV, ataxia, stroke recovery, and cancer/

chemotherapy effects on the brain

108,109

ENIGMA-Epilepsy

The ENIGMA-Epilepsy WG combined data from 24

centers across 14 countries to create the largest neuroi-

maging study to date of epilepsy

. Data from 2149 indi-

viduals with epilepsy were divided into four common

epilepsy syndromes: idiopathic generalized epilepsies (N

= 367), mesial temporal lobe epilepsies with hippocampal

sclerosis (MTLE; left, N = 415; right, N = 339), and all

other epilepsies in aggregate (N = 1026), compared to

1727 matched healthy controls. Compared to controls, all

epilepsy groups showed lower volume in the right thala-

mus (d = −0.24 to −0.73), and lower thickness in the

precentral gyri bilaterally (d = −0.34 to − 0.52). Both

Thompson et al. Translational Psychiatry Page 14 of 26

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MTLE subgroups also showed profound volume reduc-

tion in the ipsilateral hippocampus (d = −1.73 to −1.91),

and lower thickness in cortical regions, including the

precentral and paracentral gyri (d = −0.36 to −0.52)

compared to controls. Notably, the effect sizes for cortical

differences in this neurological disorder were much

greater than those seen in all complex psychiatric dis-

orders. In an approach known as ‘virtual histology’,a

follow-up study

110

overlaid the cortical deﬁcit maps on

gene-expression data from the Allen Brain Atlas, and

detected enrichment for microglial markers in regions

with greater deﬁcits. The WG is currently combining DTI

data and exploring putative neuroanatomical biomarkers

of medication treatment resistance and post-operative

outcomes.

ENIGMA-brain injury

ENIGMA’s Brain Injury WG

111

combines data from 72

centers, and is organized into ten separate subgroups that

focus on (1) acute mild traumatic brain injury (TBI), (2)

adult moderate/severe TBI

112

, (3) pediatric moderate/

severe TBI

113,114

, (4) military-related brain injury

115–118

(5) sports-related concussion

119

, (6) intimate partner

violence

120

, (7) MR spectroscopy

121

, (8) arterial spin

labeling, (9) resting state fMRI, and (10) cognitive end-

points. These groups have recently started-up compared

to other ENIGMA WGs, but are rapidly expanding in

membership and focus. In addition to meta- and mega-

analyses of relevant existing datasets, the Brain Injury

WGs endeavor to further extend efforts to promote

increased consistency in prospective data collection, both

in terms of imaging data and associated cognitive out-

come data. Additionally, the WGs are engaged in the

development of novel pipelines and analytic tools that

address brain-injury speciﬁc issues or incorporate

sequences or techniques that are potentially useful in

addressing injury associated pathology. For example,

future planned studies will compute structural pathology

proﬁles for individual TBI patients, including (i) mapping

of the heterogeneous lesions using advanced lesion

mapping methods (such as disconnectome symptom

mapping), (ii) accurate quantiﬁcation of brain atrophy (of

the different brain regions) using tensor based morpho-

metry, and (iii) identiﬁcation of subject-speciﬁc epicenters

best predictive of neurodegeneration using network

spread models. Finally, the Brain Injury WGs will inter-

face with other disease-speciﬁc WGs where comorbidity

with brain injury is high (e.g., substance use, PTSD, MDD,

ADHD), as well as with methods-focused WGs (e.g., dif-

fusion imaging, etc.). A preliminary report on 117 parti-

cipants with military-relevant blast-related versus 227

participants with non-blast related injury revealed higher

FA in veterans and service members with blast-related

injuries, and altered subcortical volumes in the group with

military TBI overall

117

. Work is ongoing to study the

effects of injuries sustained during and outside deploy-

ment, and severity and mechanisms of injury.

ENIGMA-Parkinson’s disease

ENIGMA’s Parkinson’s disease WG has analyzed scans

from 11 cohorts spanning 10 countries including 1288

patients with PD and 679 controls (age: 20–89

years)

122,123

. A PD diagnosis was associated with moder-

ately larger thalamic volumes (left: d = 0.29; right: d =

0.17) and smaller pallidal volumes (left: d = −0.25; right:

d = −0.21). There was also widespread and lower cortical

thickness in PD patients, while sparing the limbic and

insular cortices. Ongoing work on a larger sample is

relating brain structure and WM microstructure to dis-

ease severity, medication status and history and duration

of the illness as modiﬁers of these robust differences

between patients and controls.

ENIGMA-human immunode ﬁciency virus

The availability of combination antiretroviral therapy

(cART) has now transformed HIV-infection from a pos-

sibly fatal diagnosis to a chronic condition, allowing for

viral suppression and stable immune function; however,

despite inconsistencies in neuroimaging studies, neuro-

logical symptoms and consequences persist. This WG has

pooled data from 12 independent neuro-HIV studies from

Africa, Asia, Australia, Europe, and North America;

volume estimates for eight subcortical brain regions were

extracted from anatomical MRI from 1044 HIV + adults

(age: 22–81 years) to identify associations with plasma

markers reﬂecting immunosuppression (CD4+ T-cell

count) or viral load

124

. Across participants, lower cur-

rent CD4+ count was associated with smaller hippo-

campal and thalamic volumes. A detectable viral load was

also associated with smaller hippocampal (d = 0.24) and

amygdalar volumes (d = 0.18), supporting the importance

of achieving viral suppression and immune restoration.

These limbic effects are in contrast to many of the early

neuro-HIV ﬁndings that focused on basal ganglia struc-

tures, yet we found the limbic associations were largely

driven by participants on cART, while basal ganglia effects

(putamen) were detected in the subset of participants not

on cART. These ﬁndings demonstrate the continuing

effects of HIV on the brain in the current “cART era”.

Alterations in brain structures that are essential for

learning and memory has clinical signiﬁcance given

mounting evidence of HIV-associated deﬁcits in these

cognitive domains among older HIV+ adults, and the

possibility that HIV may contribute to abnormal brain

aging

125

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ENIGMA-Ataxia

This WG includes 21 sites pooling data from more than

750 individuals with inherited ataxias, including Frie-

dreich Ataxia and Spinocerebellar Ataxia (SCA) 1, 2, 3, 6,

and 7 (the poly-glutamine SCAs), alongside over 800

controls. This group is undertaking optimization and

standardization of protocols for cerebellar voxel-based

morphometry and parcellation, upper spinal cord cross-

sectional area, and brainstem volume, in line with the key

regions of pathology in these diseases. Preliminary work

indicates that gray matter degeneration principally

impacts the cerebellar anterior lobe in Friedreich ataxia,

while all areas of the cerebellum are affected in the poly-

glutamine SCAs. However, both the magnitude and pat-

tern of cerebellar gray matter degeneration are distinct

across these diseases and evolve with disease progression

and severity.

ENIGMA-stroke recovery

The ENIGMA-stroke recovery WG has addressed a

major gap in stroke research relating to the large-scale

deﬁnition of lesion masks. Researchers in this WG have

released a public archive of 304 T1-weighted MRIs with

manually segmented stroke lesion masks

126

(https://www.

icpsr.umich.edu/icpsrweb/ADDEP/studies/36684), and

developed open-source software

127

and analyses speciﬁc

for scalable

128

, reproducible lesion analyses (https://

github.com/npnl/PALS). In addition to this major meth-

odological contribution they have analyzed data from 629

participants from 22 sites worldwide to identify reliable

predictors of motor function after stroke

129,130

. They

found that motor-related subcortical volumes in the basal

ganglia and thalamus are positively associated with post-

stroke motor performance, and depend on impairment

severity, time since stroke, and lesion laterality. In con-

trast, enlarged lateral ventricles are associated with worse

post-stroke motor outcomes. The group now has data

from 1625 participants from 32 sites worldwide, and

ongoing work in the group focuses on quantifying lesion

overlap with major motor-related structures, such as the

corticospinal tracts and subcortical regions

131,132

, and

relating these measures with subcortical volumetric

measures to motor outcomes

133

ENIGMA-methods focused working groups

The ENIGMA Consortium functions as a driving force

for the development, validation and implementation of

novel methods to address the complexities of analyses of

large imaging datasets and to derive more mechanistic

insights into the processes that underpin variation in

brain organization in health and disease. To achieve this,

ENIGMA has dedicated WGs focused on the develop-

ment of more innovative pipelines for data analyses to be

applied for various dataset worldwide. The ENIGMA

Diffusion MRI WG on DTI is one of the most long-

standing. DTI offers information on microstructural

abnormalities that are not detectable on standard anato-

mical MRI. As mentioned earlier, this WG has published a

series of papers on the heritability and reproducibility of

DTI measures derived with a custom protocol based on

tract-based spatial statistics

64,134

. Diagnosis-based WGs

have used this protocol to rank effect sizes for DTI

metrics as previously described or are undertaking similar

studies including in 22q11DS

, epilepsy, PTSD

, military

TBI

113

, HIV

135

, and OCD

136

Other methodological WGs have focused on anatomical

shape analyses that enable a more precise characterization

of regional brain alterations thus resolving subregional

effects in the basal ganglia, amygdala, and hippo-

campus

55,85,137–142

. Other approaches currently used in

ENIGMA include brain structural covariance analysis

(graph theory approach for intra-individual brain struc-

tural covariance networks in OCD

77,143

, sulcal morpho-

metry, hippocampal subﬁeld analysis

80–82,144

and disease

effects on lateralization (in OCD, MDD, and ASD)

71,90,97

More recently, ENIGMA’s Brain Age WG was formed to

apply various algorithmic estimators of ‘brain age’ across

several ENIGMA WGs

. From the ENIGMA-Brain

Injury group, the MR spectroscopy (MRS) WG has

formed to focus on the harmonization of MRS data which

could reach across other WGs in the future.

The impact of ENIGMA

The ENIGMA Consortium has been a driving force in

the ﬁeld of neuroscience by making substantial con-

tributions to the science of brain variation and shaping

the working practices of the ﬁeld at various levels. In

reﬂecting on the key achievements, three areas stand out:

Promoting robustness and reproducibility

ENIGMA’s “big data” approach to neuroimaging

addresses directly the reproducibility challenges that

plague many areas of biomedical science—including

neuroscience

145–147

. Neuroimaging has received con-

siderable scrutiny regarding the reliability of published

ﬁndings, given the literature replete with studies based on

small samples and seemingly unlimited methodological

freedom

148,149

. Many other approaches also aim to tackle

this reproducibility crisis, by building data repositories

that can be accessed for replication

150–152

; yet ENIGMA

offers an opportunity to collaborate with teams of diverse

experts irrespective of whether or not any data is shared.

In one recent study by ENIGMA’s Laterality group, the

authors examined brain asymmetry in 99 MRI datasets

worldwide (from N = 17,141 people) and found that, as

expected, the reproducibility of ﬁndings increased with

the effect size and sample size, in a setting that was free

from publication bias (data available at: http://conxz.net/

Thompson et al. Translational Psychiatry Page 16 of 26

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neurohemi/)

18,153

. For example, for effect sizes of d ≥ 0.6,

the reproducibility rate was higher than 90% even when

including the datasets with sample sizes as low as 15,

while it was impossible to obtain 70% reproducibility for

small effects of d < 0.2, even with a relatively large mini-

mum sample size threshold of 500. The unprecedented

size of the datasets analyzed across ENIGMA boosts

statistical power to detect the effects of disease and their

moderators

73,154

. Through data sharing, investigators can

now identify patterns of brain abnormalities that con-

sistently characterize disorders or clinical syndromes,

while assessing their reproducibility across continents.

This is exempliﬁed by the close match between the

schizophrenia ﬁndings by ENIGMA

54,60

and independent

work by the Japanese Consortium, COCORO

and a

recent Norwegian study of 16 cohorts by Alnæs

. In all

three studies, schizophrenia patients showed enlargement

of the lateral ventricles, pallidum, putamen, and caudate,

and volume reduction in the hippocampus, amygdala,

thalamus and accumbens, with a strong agreement in the

magnitude and rank order of effects from highest to least

group difference. Similarly, a recent GWAS study of the

UK Biobank dataset

155

was able to replicate the majority

of the genetic loci discovered by ENIGMA in two separate

GWAS of subcortical volumes

24,25

. Thus, the interna-

tional, multi-site nature of ENIGMA studies likely pro-

motes representative ﬁndings that are widely

generalizable. Meanwhile, the larger and more diverse

samples are valuable resources for understanding the

heterogeneity across different studies, and may provide

new insights into the reproducibility issue faced by the

neuroimaging community. Moreover, ENIGMA offers a

platform for investigators to converge on methods for

sharing and analyzing data acceptable to the community.

ENIGMA also offers new opportunities to change the

landscape for how data can be used. In current research

practices, a great resource of data remains largely

untapped that is often known as “long-tail” data: data sets

collected in individual laboratories that accumulate over

many years and funding cycles

156

. Much valuable data

remains dormant (and unpublished) due to a lack of

personnel and time to analyze it, and this is going to

increase with studies including larger samples than before.

Efforts through ENIGMA to leverage ‘dormant’ data in

labs throughout the world have at least three important

advantages. First, data sharing increases the scope of the

science, enhancing opportunities for analyses not other-

wise possible with small sample sizes. Second, data shar-

ing naturally engages scientists from distinct disciplines—

a crucial step for advancing the clinical neurosciences

157

A ﬁnal beneﬁt that is sometimes overlooked in global

scientiﬁc collaborations is their power to build and

enhance diplomatic relations and transcend political

conﬂicts between nations

158

. With representation from 43

countries—some of which have minimal diplomatic ties—

collaborations are not only constructive in terms of col-

lective problem solving, but they also build connections

between high income countries and the poorest nations

across the globe and to build capacity in the latter

158

Setting methodological standards

The ENIGMA Consortium has provided a blueprint for

multi-site standardization in terms of mining legacy

neuroimaging and genetic data. The success of this

approach is obvious when considering the volume of over

50 published works that has relied on the ENIGMA

pipelines. Furthermore, funding bodies, such as the

National Institutes of Health in the United States, have

gained interest in such approaches; program announce-

ments requesting applications on aggregating existing

biomedical data, or making use of existing resources, have

become increasingly common. Moving forward, ENIGMA

remains a test-bed of unprecedented scale and power for

developing and benchmarking novel analytic methods.

This contribution is of paramount importance as

advanced statistical modeling and bioinformatics become

essential for analytic pipelines. In efforts complementary

to the technical advances made in multisite data collec-

tion initiatives such as the Alzheimer’s Disease Neuroi-

maging Initiative, the Human Connectome Project, and

the UK Biobank, ENIGMA’s studies have required us to

continually develop new scientiﬁc approaches to analyze

data from diverse and independent populations. ENIGMA

has made several technical advances that may be adapted

to other domains, including creating, adapting, and

extensively testing harmonized methods for distributed

analysis, meta-analysis, and cross-site data integration

(see Supplementary Appendix A: Technical Contributions

of ENIGMA).

Driving discovery

Neuroimaging and genetics are ﬁelds of both large and

small effects. For common, complex chronic diseases,

effects on brain metrics can be very subtle, but for rare

monogenic disorders and across the ﬁeld of neurology—

including epilepsy, brain injury, stroke and neuro-oncol-

ogy, disease effects can be relatively large (although not

exclusively). In the 10 years since ENIGMA was founded,

the primary lesson has been on the power of worldwide

collaboration to discern subtle patterns in brain data, and

advance neuroscience beyond the capacity of any one

group of researchers collecting data on their own. New

discoveries regarding the factors that inﬂuence brain

organization and its association with health and disease

are predicated on having adequate statistical power and

on developing new neuroimaging approaches aiming to

lead to more mechanistic explanations of the multi-scale

organization of the brain.

Thompson et al. Translational Psychiatry Page 17 of 26

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Challenges and future directions

Even given the advances made through ENIGMA dur-

ing its ﬁrst decade, as a growing consortium, ENIGMA

faces important challenges. Thus far, ENIGMA has largely

relied on existing data, which implies a degree of het-

erogeneity with respect to phenotyping—including clin-

ical assessments, scanners and imaging protocols.

Another limitation of this type of data is that the depth of

phenotyping varies across centers, which can lead to a

limited set of clinical and other scales shared by all cen-

ters. As we discuss below, ENIGMA is now beginning to

address these limitations with a series of newly funded

and planned studies

111,112,114,118–121

. The paucity of

longitudinal data in the literature is also reﬂected within

ENIGMA, which includes a limited number of long-

itudinal studies. Consequently, the data-driven approach

used in ENIGMA is complementary, but not always

superior, to well-designed, hypothesis-driven, smaller-

scale prospective single-center or multi-center studies

with in-depth phenotyping.

Extending imaging modalities and computational

approaches

ENIGMA’s future developments will include the coor-

dinated analyses of new data modalities (such as resting

state and task-related functional MRI

159–162

, as well as

geostatistical and mobile sensor data), and deeper or more

reﬁned analyses of current imaging modalities. Diffusion

MRI, in particular, is moving towards multi-shell proto-

cols that can better differentiate cellular and

microstructural sources of variance that may explain

patterns observed with DTI

. Multimodal projects that

pool data across imaging modalities are likely to boost the

accuracy of machine learning methods for differential

diagnosis, outcome prediction, and subtyping. Unsu-

pervised learning—applied to imaging and clinical data—

may also help to identify homogeneous subgroups within

and across disorders. Deep learning, for example, beneﬁts

from very large datasets, such as those analyzed in

ENIGMA, and these and other artiﬁcial intelligence

methods show promise in identifying unsuspected fea-

tures and patterns in images beyond those derived using

traditional methods. From its inception, ENIGMA has

accommodated varying data sharing practices across

institutions and countries, has used strategies (such as

meta-analysis) to overcome some of these, and is working

with ﬁeld experts on novel strategies (like COINSTAC or

other distributed analysis approaches)

163

to allow for

more powerful analysis without sending data around the

globe. On the ‘omics’ side, whole genome sequencing

promises to reﬁne our understanding of causal loci across

all phenotypes, from plasma markers and brain metrics to

environmental exposure and clinical measures of disease

burden.

Cross-disorder analyses

ENIGMA has recently created cross-disorder groups to

answer transdiagnostic questions that draw on data from

multiple WGs

164

. An exemplar of this approach is the

newly formed ENIGMA-Relatives WG which examines

Fig. 7 Topology of large-scale scientiﬁc collaboration. a The topology of scientiﬁc collaboration in ENIGMA has some properties that resemble a

modular hierarchical network (Ravasz and Barabasi

199

, Slaughter

200

). In this diagram (a), nodes represent individual scientists working on a project,

and links denote active scientiﬁc collaborations (that might result in co-authored publications, like this review, for example). ENIGMA’s WGs resemble

the yellow sets of nodes: guided by a small group of WG chairs, several clusters of scientists coordinate projects applying various methods to the

same datasets (e.g., MRI and DTI meta-analysis, machine learning, and modeling of clinical outcomes). WGs study different disorders with the same

harmonized methods, enabling to cross-disorder collaborations across WGs. The modular organization allows independent and coordinated projects

to proceed in parallel, distributing work and coordination, without requiring a central hub for all communication. Real clusters may differ in their

number of members and links [(b) shows a different graph with a similar hierarchical modular form], and may change dynamically over time as new

groups and projects form and projects end.

Thompson et al. Translational Psychiatry Page 18 of 26

UNCORRECTED PROOF

brain organization in the unaffected ﬁrst-degree relatives

of patients with psychiatric disorders. The ﬁrst study from

this group focused on identifying common and distinct

anatomical patterns in patients with SCZ (N = 1016) or

BD (N = 666) and their unaffected relatives (N for SCZ

relatives = 1228 and for BD relatives = 852)

remarkable ﬁnding from this study is that the ﬁrst-degree

relatives of BD patients had larger ICV compared to

controls (d = 0.16) while ﬁrst-degree relatives of SCZ

patients had smaller ICV and lower cortical thickness, and

when controlling for ICV, had regionally smaller thalamic

volumes. Other newly formed groups aim to aggregate

data across the spectrum of mental illness that may be

prone to similar symptoms and outcomes, such as suicidal

ideations and actions. Cross-disorder initiatives have

formed within ENIGMA as partnerships between existing

group members. The topology of collaboration (Fig. 7)

includes working partnerships between group members

working on similar problems, providing natural connec-

tions between topics of study of the different groups.

Sex differences

ENIGMA’s sex differences Initiative is probing disease

WG datasets to better understand sex disparities in risk

factors, disease effects, or outcomes and their relationship

with brain organization. In a new initiative, the ENIGMA-

Transgender WG is contributing additional insights with

respect to the biological underpinnings of sex assigned at

birth versus gender identity

165

. The ﬁrst study from this

group was based on more than 800 scans and pooled

various MRI-based measures (cortical thickness, surface

area, and volume) across eight international sites. While

effects varied depending on the morphometric measure

applied and the brain regions considered, a general pat-

tern emerged: transgender men (assigned female at birth)

mostly resemble cis-gender women, whereas transgender

women (assigned male at birth) range between cis-gender

men and cis-gender women

166

. Ongoing initiatives

examining sex-differences focus on sex-speciﬁc GWAS

studies, and developmental and aging trajectories.

Global health disparities

Health disparities, including those that exist in low and

middle income countries, are also a topic of great interest

for ENIGMA, as prevalence, treatments, and access to

healthcare varies within and across countries. While the

analyses in ENIGMA so far tend to show cross-national

agreement in brain signatures and associated genetic loci

of various psychiatric diseases, more in-depth phenotyp-

ing may reveal circumstances where risk factors apply

more strongly to speciﬁc ethnic or sociodemographic

groups, and means to remediate them, consistent with the

concept of precision public health.

In closing, we reiterate ENIGMA’s mission statement,

which reads : “Individually, we contribu te l ittle to the

quest for truth, but working together, the whole vast

world of science is within our reach.”

(Aristotle, 350

BCE)

69,134,167–196

Acknowledgements

The work reported here was supported in part by many public and private

agencies across the world. Individual authors’ funding is listed in

Supplementary Appendix B. Core funding for ENIGMA was provided by the NIH

Big Data to Knowledge (BD2K) program under consortium grant U54

EB020403, by the ENIGMA World Aging Center (R56 AG058854), and by the

ENIGMA Sex Differences Initiative (R01 MH116147). Additional support was

provided by grants to the ENIGMA-PGC PTSD Working Group (R01 MH111671;

PI: RAM), the ENIGMA-Addiction Working Group (R01 DA047119; to H.P.G. and

P.J.C.), the ENIGMA Suicidal Thoughts and Behavior Working Group (R01

MH117601; to N.J. and L.S.), the ENIGMA Epilepsy Working Group (R01

NS107739; to C.R.M.), a genotyping grant from the Australian NHMRC

(APP1103623 and APP1158127; to SEM), a German federal grant to the ENIGMA

Task-Related fMRI Group (ER724/4-1 and WA1539/11-1; to H.W. and I.M.V.), a

Kavli Foundation Neuroscience without Borders seed grant (to N.J. and P.M.T.),

an NIH instrumentation grant (S10 OD023696 to P.K.), and K01 HD091283 (to S.

L.L.). We thank all scientists and participants in ENIGMA who made this work

possible.

Conﬂict of interest

Individual authors’ disclosures and conﬂicts of interest are listed in

Supplementary Appendix C.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in

published maps and institutional afﬁliations.

Supplementary Information accompanies this paper at (https://doi.org/

10.1038/s41398-020-0705-1).

Received: 3 July 2019 Revised: 11 December 2019 Accepted: 20 December

2019.

Published online: xx xxx 2020

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Author details

Paul M. Thompson

, Neda Jahanshad

, Christopher R. K. Ching

,LaurenE.Salminen

, Sophia I. Thomopoulos

Joanna Bright

, Bernhard T. Baune

2,3,4

, Sara Bertolín

, Janita Bralten

6,7

, Willem B. Bruin

, Robin Bülow

, Jian Chen

Yann Chye

,UdoDannlowski

,CarolienG.F.deKovel

12,13

, Gary Donohoe

,LisaT.Eyler

15,16

,StephenV.Faraone

Pauline Favre

18,19

, Courtney A. Filippi

,ThomasFrodl

21,22,23

, Daniel Garijo

,YolandaGil

24,25

,HansJ.Grabe

26,27

Katrina L. Grasby

,TomasHajek

29,30

, Laura K. M. Han

,SeanN.Hatton

32,33

, Kevin Hilbert

, Tiffany C. Ho

35,36

Laurena Holleran

,GeorgHomuth

,NorbertHosten

, Josselin Houenou

18,19,38

, Iliyan Ivanov

, Tianye Jia

40,41,42

Sinead Kelly

43,44

, Marieke Klein

6,7,45

,JunSooKwon

46,47

, Max A. Laansma

, Jeanne Leerssen

, Ulrike Lueken

Abraham Nunes

29,50

,JosephO’ Neill

, Nils Opel

, Fabrizio Piras

, Federica Piras

, Merel C. Postema

, Elena Pozzi

53,54

Natalia Shatokhina

, Carles Soriano-Mas

5,55,56

, Gianfranco Spalletta

52,57

,DaqiangSun

58,59

, Alexander Teumer

Amanda K. Tilot

, Leonardo Tozzi

, Celia van der Merwe

61,62

,EusJ.W.VanSomeren

49,63

,GuidoA.vanWingen

Henry Völzke

60,64

,EstherWalton

,LeiWang

66,67

, Anderson M. Winkler

, Katharina Wittfeld

26,27

,MargaretJ.Wright

68,69

Je-Yeon Yun

70,71

, Guohao Zhang

, Yanli Zhang-James

17,73

,BhimM.Adhikari

, Ingrid Agartz

75,76,77

, Moji Aghajani

78,79

André Aleman

, Robert R. Althoff

,AndreAltmann

, Ole A. Andreassen

75,83

,DavidA.Baron

Brenda L. Bartnik-Olson

, Janna Marie Bas-Hoogendam

86,87,88

, Arielle R. Baskin-Sommers

, Carrie E. Bearden

58,90

Laura A. Berner

, Premika S. W. Boedhoe

,RachelM.Brouwer

, Jan K. Buitelaar

, Karen Caeyenberghs

CharlotteA.M.Cecil

93,94

, Ronald A. Cohen

95,96

,JamesH.Cole

97,98

,PatriciaJ.Conrod

,StephaneA.DeBrito

100

Sonja M. C. de Zwarte

, Emily L. Dennis

1,101,102

, Sylvane Desrivieres

103

, Danai Dima

104,105

, Stefan Ehrlich

106

Carrie Esopenko

107

, Graeme Fairchild

, Simon E. Fisher

7,13

, Jean-Paul Fouche

108,109

,ClydeFrancks

7,13

Sophia Frangou

110,111

, Barbara Franke

6,7,112

, Hugh P. Garavan

113

,DavidC.Glahn

114,115

,NynkeA.Groenewold

108

Tiril P. Gurholt

,BorisA.Gutman

116,117

,TimHahn

118

, Ian H. Harding

119

, Dennis Hernaus

120

,DerrekP.Hibar

121

Frank G. Hillary

122,123

, Martine Hoogman

6,7

, Hilleke E. Hulshoff Pol

, Maria Jalbrzikowski

124

, George A. Karkashadze

125

Eduard T. Klapwijk

86,88

, Rebecca C. Knickmeyer

126,127,128

, Peter Kochunov

,IngaK.Koerte

102,129

, Xiang-Zhen Kong

Sook-Lei Liew

130,131

, Alexander P. Lin

132,133

,MarkW.Logue

134,135,136

, Eileen Luders

137,138

, Fabio Macciardi

139

Scott Mackey

113

,AndrewR.Mayer

140

, Carrie R. McDonald

32,141

, Agnes B. McMahon

1,142

, Sarah E. Medland

Gemma Modinos

143

,RajendraA.Morey

144,145

, Sven C. Mueller

146,147

, Pratik Mukherjee

148

Leyla Namazova-Baranova

149,150

, Talia M. Nir

,AlexanderOlsen

151,152

, Peristera Paschou

153

, Daniel S. Pine

154

Fabrizio Pizzagalli

,MiguelE.Rentería

155

, Jonathan D. Rohrer

156

, Philipp G. Sämann

157

,LianneSchmaal

54,158

Gunter Schumann

42,159

,MarkS.Shiroishi

1,160

, Sanjay M. Sisodiya

161,162

,DirkJ.A.Smit

, Ida E. Sønderby

,DanJ.Stein

163

Jason L. Stein

164

, Masoud Tahmasian

165

,DavidF.Tate

166,167

, Jessica A. Turner

168

, Odile A. van den Heuvel

48,78

NicJ.A.vanderWee

87,88

, Ysbrand D. van der Werf

,TheoG.M.vanErp

169,170

, Neeltje E. M. van Haren

45,93

Daan van Rooij

171

, Laura S. van Velzen

54,158

, Ilya M. Veer

172

,DickJ.Veltman

, Julio E. Villalon-Reina

,HenrikWalter

172

Christopher D. Whelan

173,174

, Elisabeth A. Wilde

101,175,176

, Mojtaba Zarei

165

and Vladimir Zelman

177,178

,fortheENIGMA

Consortium

Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del

Rey, CA, USA.

Department of Psychiatry, University of Münster, Münster, Germany.

Department of Psychiatry, The University of Melbourne, Melbourne, VIC,

Australia.

The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

Department of Psychiatry, Bellvitge

University Hospital, Bellvitge Biomed ical Research Institute-IDIBELL, Barcelona, Spain.

Department of Human Genetics, Radboud University Medical Center,

Nijmegen, The Netherlands.

Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Department of Psychiatry,

Thompson et al. Translational Psychiatry Page 24 of 26

UNCORRECTED PROOF

Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.

Institute for Diagnostic Radiology and Neuroradiology,

University Medicine Greifswald, Greifswald, Germany.

Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.

Turner

Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia.

Biometris Wageningen University and Research,

Wageningen, The Netherlands.

Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.

The Center for

Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland, Galway, Ireland.

Department of Psychiatry, University of California,

San Diego, La Jolla, CA, USA.

Desert-Paciﬁc Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA.

Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA.

INSERM Unit 955 Team 15 ‘Translational

Psychiatry’, Créteil, France.

NeuroSpin, UNIACT Lab, Psychiatry Team, CEA Saclay, Gif-Sur-Yvette, France.

National Institute of Mental Health, National of Health,

Bethesda, MD, USA.

Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany.

Department of Psychiatry,

Trinity College Dublin, Dublin, Ireland.

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.

Information Sciences Institute, University

of Southern California, Marina del Rey, CA, USA.

Department of Computer Science, University of Southern California, Los Angeles, CA, USA.

Department of

Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.

German Center for Neurodegenerative Diseases (DZNE), Site Rostock/

Greifswald, Greifswald, Germany.

Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Department of Psychiatry, Dalhousie

University, Halifax, NS, Canada.

National Institute of Mental Health, Klecany, Czech Republic.

Department of Psychiatry, Amsterdam University Medical Centers,

VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands.

Center for Multimodal Imaging and Genetics, University of

California, San Diego, La Jolla, CA, USA.

Brain and Mind Centre, University of Sydney, Sydney, Australia.

Department of Psychology, Humboldt-Universität zu

Berlin, Berlin, Germany.

Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.

Department of Psychiatry & Weill Institute for

Neurosciences, University of California, San Francisco, San Francisco, CA, USA.

Interfaculty Institute for Genetics and Functi onal Genomics, University Medicine

Greifswald, Greifswald, Germany.

APHP, Mondor University Hospitals, School of Medicine, DMU Impact, Psychiatry Department, Créteil, France.

Icahn School of

Medicine at Mount Sinai, New York, NY, USA.

Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

MOE Key

Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Centre for Population Neuroscience and Precision

Medicine (PONS), MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK.

Department of Psychiatry, Beth Israel

Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA.

Department

of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Department of Psychiatry, Seoul National

University College of Medicine, Seoul, Republic of Korea.

Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences,

Seoul, Republic of Korea.

Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The

Netherlands.

Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.

Faculty of Computer Science,

Dalhousie University, Halifax, NS, Canada.

Child & Adolescent Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA.

Laboratory of

Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne,

Melbourne, VIC, Australia.

Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia.

CIBERSAM-G17, Madrid, Spain.

Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain.

Department of Psychiatry and

Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and

Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.

Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare

System, Los Angeles, CA, USA.

Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.

Stanley Center for Psychiatric Research,

The Broad Institute, Cambridge, MA, USA.

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.

Psychiatry and Integrative

Neurophysiology, VU University, Amsterdam UMC, Amsterdam, The Netherlands.

German Centre for Cardiovascular Research, Partner Site Greifswald, Greifswald,

Germany.

Department of Psychology, University of Bath, Bath, UK.

Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine,

Chicago, IL, USA.

Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Queensland Brain Institute, University of Queensland,

Brisbane, QLD, Australia.

Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.

Seoul National University Hospital, Seoul, Republic of

Korea.

Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.

Department of Computer Science and

Electrical Engineering, University of Maryland, Baltimore County, MD, USA.

Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University,

Syracuse, NY, USA.

Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.

Norwegian Centre for Mental Disorders Research

(NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Department of

Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden.

Department of Psychiatric Research, Diakonhjemmet Hospital,

Oslo, Norway.

Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.

Department

of Research & Innovation, GGZ InGeest, Amsterdam, The Netherlands.

University of Groningen, University Medical Center Groningen, Groningen, The

Netherlands.

Psychiatry, Pediatrics, and Psychological Sciences, University of Vermont, Burlington, VT, USA.

Centre for Medical Image Computing (CMIC),

Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

Department of Mental Health and Addiction, Oslo

University Hospital, Oslo, Norway.

Provost and Senior Vice President, Western University of Health Sciences, Pomona, CA, USA.

Department of Radiology, Loma

Linda University Medical Center, Loma Linda, CA, USA.

Institute of Psychology, Leiden University, Leiden, The Netherlands.

Department of Psychiatry, Leiden

University Medical Center, Leiden, The Netherlands.

Leiden Institute for Brain and Cognition, Leiden, The Netherlands.

Department of Psychology, Yale

University, New Haven, CT, USA.

Department of Psychology, University of California, Los Angeles, CA, USA.

Department of Cognitive Neuroscience, Donders

Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Cognitive Neuroscience Unit, School of Psychology,

Deakin University, Burwood, VIC, Australia.

Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands.

Department of Epidemiology, Erasmus Medical Centre, Rotterdam, The Netherlands.

Center for Cognitive Aging and Memory, University of Florida, Gainesville,

FL, USA.

Clinical and Health Psychology, Gainesville, FL, USA.

Centre for Medical Image Computing (CMIC), Department of Computer Science, University College

London, London, UK.

Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Universite de Montreal, Centre de Recherche

CHU Ste-Justine, Montreal, QC, Canada.

100

School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK.

101

Department of

Neurology, University of Utah, Salt Lake City, UT, USA.

102

Psychiatry Neuroimaging Laboratory, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA,

USA.

103

Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK.

104

Department

of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK.

105

Department of Neuroimaging, Institute of Psychology,

Psychiatry and Neurosciences, King’s College London, London, UK.

106

Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of

Medicine, TU Dresden, Dresden, Germany.

107

School of Health Professions, Rutgers Biomedical Health Sciences, Newark, NJ, USA.

108

Department of Psychiatry and

Mental Health, University of Cape Town, Cape Town, South Africa.

109

SU/UCT MRC Unit on Risk & Resilience in Mental Disorders, University of Stellenbosch,

Stellenbosch, South Africa.

110

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

111

University of British Columbia, Vancouver,

Canada.

112

Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands.

113

Department of Psychiatry, University of Vermont,

Burlington, VT, USA.

114

Department of Psychiatry, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA.

115

Olin Neuropsychiatric Research

Center, Institute of Living, Hartford, CT, USA.

116

Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.

117

Institute for Information Transmission

Problems, Kharkevich Institute, Moscow, Russian Federation.

118

Institute of Translational Psychiatry, University of Münster, Münster, Germany.

119

Turner Institute for

Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.

120

Department of Psychiatry & Neuropsychology, School

for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.

121

Genentech, Inc., South San Francisco, CA, USA.

122

Department of

Thompson et al. Translational Psychiatry Page 25 of 26

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Psychology, Penn State University, University Park, PA, USA.

123

Social Life and Engineering Sciences Imaging Center, University Park, PA, USA.

124

Department of

Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

125

Central Clinical Hospital of the Russian Academy of Sciences, Moscow, Russian Federation.

126

Department of Pediatrics, Michigan State University, East Lansing, MI, USA.

127

Institute for Quantitative Health Science and Engineering, East Lansing, MI, USA.

128

Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

129

CBRAIN, Department of Child and Adolescent Psychiatry,

Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany.

130

Stevens Neuroimaging and Informatics Institute, Keck School

of Medicine, University of Southern California, Los Angeles, CA, USA.

131

Chan Division of Occupational Science and Occupational Therapy, Los Angeles, CA, USA.

132

Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Boston, MA, USA.

133

Harvard Medical School, Boston, MA, USA.

134

National Center for PTSD at

Boston VA Healthcare System, Boston, MA, USA.

135

Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA.

136

Biomedical Genetics,

Boston University School of Medicine, Boston, MA, USA.

137

School of Psychology, University of Auckland, Auckland, New Zealand.

138

Laboratory of Neuro Imaging,

Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

139

Department of

Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA.

140

Mind Research Network, Albuquerque, NM, USA.

141

Psychiatry, San Diego, CA,

USA.

142

The Kavli Foundation, Los Angeles, CA, USA.

143

Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London,

London, UK.

144

Department of Psychiatry, Duke University School of Medicine, Durham, NC, USA.

145

Mental Illness Research Education and Clinical Center, Durham

VA Medical Center, Durham, NC, USA.

146

Experimental Clinical & Health Psychology, Ghent University, Ghent, Belgium.

147

Department of Personality, Psychological

Assessment and Treatment, University of Deusto, Bilbao, Spain.

148

Radiology and Biomedical Imaging, San Francisco, CA, USA.

149

Department of Pediatrics (Head),

Russian National Research Medical University MoH RF, Moscow, Russian Federation.

150

Center of Pediatrics, Central Clinical Hospital, MoS High Education RF,

Moscow, Russian Federation.

151

Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway.

152

Department of Physical

Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.

153

Biological Sciences, Purdue University, West Lafayette, IN,

USA.

154

National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA.

155

Department of Genetics and Computational Biology, QIMR

Berghofer Medical Research Institute, Brisbane, QLD, Australia.

156

Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London,

UK.

157

Max Planck Institute of Psychiatry, Munich, Germany.

158

Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

159

Department of Psychiatry and Psychotherapy, Charite, Humboldt University, Berlin, Germany.

160

Department of Radiology, Keck School of Medicine of USC,

University of Southern California, Los Angeles, CA, USA.

161

Department of Clinical and Experimental Epilepsy , University College London, London, UK.

162

Chalfont

Centre for Epilepsy, Chalfont St Peter, UK.

163

Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town,

South Africa.

164

Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

165

Institute of Medical

Science and Technology, Shahid Beheshti University, Tehran, I. R., Iran.

166

Department of Neurology, TBI and Concussion Center, Salt Lake City, UT, USA.

167

Missouri

Institute of Mental Health, Berkeley, MO, USA.

168

Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA.

169

Clinical

Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.

170

Center for the

Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, USA.

171

Donders Centre for Cognitive Neuroimaging, Radboud University Medical

Centre, Nijmegen, The Netherlands.

172

Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin,

corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

173

Molecular and Cellular Therapeutics,

Royal College of Surgeons in Ireland, Dublin, Ireland.

174

Research and Early Development, Biogen Inc, Cambridge, MA, USA.

175

VA Salt Lake City Healthcare System,

Salt Lake City, UT, USA.

176

Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA.

177

Keck School of Medicine,

University of Southern California, Los Angeles, CA, USA.

178

Skolkovo Institute of Science and Technology, Moscow, Russian Federation

Thompson et al. Translational Psychiatry Page 26 of 26

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